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Photorefractive Volume Holography in Artificial Neural Networks


Brady, David Jones (1990) Photorefractive Volume Holography in Artificial Neural Networks. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/1YB6-SE42.


This thesis describes the use of volume holography to implement large-scale linear transformations on distributed optical fields. Such transformations are useful in the construction of hardware for artificial neural networks. The reconstruction of multiple grating holograms in layers of thin transparencies and in continuous volume media is considered and conditions under which such holograms may be used for linear transformations are derived. The control of the nature of the transformation implemented using fractal sampling grids is reviewed and the impact of such sampling grids on the energy efficiency of the overall system is considered. Information storage in volume holograms is shown to require multiple exposures and the impact of multiple exposures on linear hologram formations in saturable media and photorefractive materials is considered. It is shown for both types of media that the overall diffraction efficiency of a recorded hologram must decrease with the square of the rank of the transformation implemented. A theory for hologram formation in photorefractive materials with multiple trapping species is developed and compared with experimental results. The impact of multiple species and fixing mechanisms on linear hologram formation is evaluated. A method for refreshing the diffraction efficiency of photorefractive holograms in adaptive systems is described and demonstrated. The construction of thick holograms for linear transformations in waveguides is considered. A novel method for controlling such holograms is described and demonstrated. Learning in holographic neural networks is considered and two experimental holographic neural systems are described. The relative strengths of optical and electronic technologies for implementations of neural interconnections are considered.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:artificial neural networks; barium titanate; holography; lithium niobate
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Applied Physics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Psaltis, Demetri (advisor)
  • Bellan, Paul Murray (co-advisor)
Thesis Committee:
  • Psaltis, Demetri (chair)
  • Kimble, H. Jeff
  • Hopfield, John J.
  • Vahala, Kerry J.
  • Bellan, Paul Murray
  • Abu-Mostafa, Yaser S.
  • Bridges, William B.
Defense Date:19 December 1989
Non-Caltech Author Email:dbrady (AT)
Funding AgencyGrant Number
Office of Naval Research (ONR)UNSPECIFIED
Record Number:CaltechETD:etd-05022006-155139
Persistent URL:
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:1577
Deposited By: Imported from ETD-db
Deposited On:02 May 2006
Last Modified:26 Jan 2022 18:04

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